We address the problem of passive localisation of a stationary, ground-level IoT radio emitter using Doppler frequency measurements collected by low-Earth orbit (LEO) satellites during an observation window. The problem is challenging because radio emission from low-cost IoT devices is affected by various compounding sources of measurement error, that collectively render the likelihood function intractable in a closed form. Hence, we apply and investigate the performance of Approximate Bayesian Computation (ABC) methods for this task. Numerical results demonstrate the statistical and computational performance of two ABC methods, rejection sampling ABC and sequential Monte Carlo ABC.
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